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Background <p>Lymphovascular invasion (LVI) is a well-established adverse prognostic factor in prostate cancer (PCa). This study aimed to develop and validate an artificial intelligence (AI)-based framework leveraging multi-instance learning (MIL) and foundation models for accurate and interpretable prediction of LVI in prostate cancer using whole-slide images (WSIs).</p> Methods <p>A weakly supervised deep-learning pipeline based on the clustering-constrained attention MIL framework was implemented to analyze hematoxylin and eosin (H&amp;E)-stained WSIs from two independent cohorts: 280 patients from Renmin Hospital of Wuhan University (RHWU) and 340 patients from The Cancer Genome Atlas (TCGA). Feature extraction was performed using pretrained encoders including UNI-v2, CONCH, and ResNet-50. Attention heatmaps were used to interpret model focus, whereas biologic correlates of model predictions were explored through differential expression analysis and gene ontology (GO) enrichment.</p> Results <p>The proposed models achieved strong predictive performance, with UNI-v2 outperforming the other encoders (area under the curve [AUC], 0.839 for RHWU and 0.854 for TCGA). Attention-based interpretability highlighted high-risk histopathologic regions characterized by hyperchromatic nuclei, prominent nucleoli, and increased mitotic activity. Exploratory transcriptomic analysis showed 381 differentially expressed genes (DEGs) between LVI-positive and LVI-negative groups. Gene ontology enrichment showed that upregulated DEGs in the LVI-positive group were enriched in mitotic and immune-related pathways, whereas downregulated genes were associated with ion transport.</p> Conclusions <p>This study exhibited a robust and interpretable AI framework for predicting LVI in PCa from WSIs using weakly supervised learning and domain-adapted foundation models. The model achieved high accuracy, provided biologically meaningful insights, and showed potential for clinical translation as a decision-support tool in precision pathology.</p>

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Foundation Model and Multi-Instance Learning-Based Framework for Predicting Lymphovascular Invasion in Prostate Cancer

  • 期刊论文

  • 发表日期：

    2026年3月12日

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  • Qingyuan Zheng,
  • Haonan Mei,
  • Dan Wang,
  • Xiuheng Liu,
  • Lei Wang,
  • Zhiyuan Chen

摘要

Background

Lymphovascular invasion (LVI) is a well-established adverse prognostic factor in prostate cancer (PCa). This study aimed to develop and validate an artificial intelligence (AI)-based framework leveraging multi-instance learning (MIL) and foundation models for accurate and interpretable prediction of LVI in prostate cancer using whole-slide images (WSIs).

Methods

A weakly supervised deep-learning pipeline based on the clustering-constrained attention MIL framework was implemented to analyze hematoxylin and eosin (H&E)-stained WSIs from two independent cohorts: 280 patients from Renmin Hospital of Wuhan University (RHWU) and 340 patients from The Cancer Genome Atlas (TCGA). Feature extraction was performed using pretrained encoders including UNI-v2, CONCH, and ResNet-50. Attention heatmaps were used to interpret model focus, whereas biologic correlates of model predictions were explored through differential expression analysis and gene ontology (GO) enrichment.

Results

The proposed models achieved strong predictive performance, with UNI-v2 outperforming the other encoders (area under the curve [AUC], 0.839 for RHWU and 0.854 for TCGA). Attention-based interpretability highlighted high-risk histopathologic regions characterized by hyperchromatic nuclei, prominent nucleoli, and increased mitotic activity. Exploratory transcriptomic analysis showed 381 differentially expressed genes (DEGs) between LVI-positive and LVI-negative groups. Gene ontology enrichment showed that upregulated DEGs in the LVI-positive group were enriched in mitotic and immune-related pathways, whereas downregulated genes were associated with ion transport.

Conclusions

This study exhibited a robust and interpretable AI framework for predicting LVI in PCa from WSIs using weakly supervised learning and domain-adapted foundation models. The model achieved high accuracy, provided biologically meaningful insights, and showed potential for clinical translation as a decision-support tool in precision pathology.